Published November 17, 2025 | Version v1
Dissertation Open

Estimation of Chlorophyll-a from Case-2 Inland Waters: Comparing Two Analytical Models

Authors/Creators

  • 1. ROR icon University of Cape Coast

Description

Inland waters play an important role in human lives, providing water for drinking, irrigation, and industrial use. The paper draws on two different reflective band-ratio algorithms, namely the Maximum Chlorophyll-a Index (MCI) and New Three Band Algorithm (N3B), to estimate Chlorophyll-a concentrations from Landsat-8 images and water samples. Band tuning procedures were performed to find optimal peak wavelength(s) suitable for the estimation of Chl-a on Landsat-8 satellite images and spectrometric data. Furthermore, the Maximum Chlorophyll-a Index (MCI) and New Three Band Algorithm (N3B) were compared, using statistical regression models such as Coefficient of Determination (R2), Relative Mean Absolute Error (rMAE), and Root Mean Square Error (RMSE) to find out which algorithm performed best in estimating Chl-a based on the variations of the in-situ and modelled Chlorophyll-a pigments data. The results demonstrated a better performance of the MCI algorithm as compared to N3B based on the minimum Coefficient of Determination (R2), Relative Mean Absolute Error (rMAE), and Root Mean Square Error (RMSE) after data regression applied on both in-situ and concurrent Landsat-8 data. The MCI algorithm performed well on both in-situ Chl-a with an R2 of 0.69 and a minimal percentage error (rMAE) of 18.34% and Landsat-8 Chl-a with an R2 of 0.75 and a minimal percentage error (rMAE) of 21.29%, respectively. On the other hand, the N3B algorithm had an R2 of 0.54 when applied on in-situ data and an R2 of 0.65 when applied on Landsat-8 data. The standard error for MCI was comparatively lower than that of N3B for Landsat-8 data; hence, in this study, the MCI model emerged as the best-performing algorithm. In all, although both algorithms were sensitive in the estimation of Chlorophyll-a contents, as the paper presented, the MCI algorithm is more sensitive for the retrieval of chlorophyll-a concentration from Case-2 inland waters using both in-situ and Landsat-8 data.

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References

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